In today's business and scientific world, color has become essential as a component of communication. Color facilitates the sharing of knowledge and ideas. Companies involved in the development of digital color print engines are continuously looking for ways to improve the total image quality of their products. One of the elements that affects image quality is the ability to consistently produce the same quality image output on an imaging device or printer from one day to another, from one week to the next, month after month. Colors on a printer tend to drift over time due to ink/toner variations, temperature fluctuations, type of media used, environment, etc. There has been a long felt commercial need for efficiently maintaining print color predictability, particularly as electronic marketing has placed more importance on the accurate representation of merchandise in illustrative print or display media.
Color printing characterization is a crucial task in color management. The characterization process essentially establishes a relationship between device dependent, e.g. printer CMY, and device independent, e.g. CIELAB values. Several color management tasks such as derivation of ICC profiles, color transforms for calibration, etc. benefit from an accurate mathematical characterization of the physical device. For color printers, characterization is an expensive process involving large numbers of patch measurements and subsequent computation to derive satisfactorily accurate color lookup-tables (LUTs). Further, this process is halftone dependent, i.e. patch printing, measuring and associated computation scales proportionally with the number of halftoning methods. Most high-end color printers are equipped with multiple halftone screens and hence a method for printer characterization that can minimize required patch measurement is very desirable.
Color printer characterization is the process of deriving a mathematical transform which relates printer CMY(K) to its corresponding device independent representation, e.g. spectral, CIELAB, etc. The forward characterization transform defines the response of the device to a known input, thus describing the color characteristics of the device. The inverse characterization transform compensates for these characteristics and determines the input to the device that is required to obtain a desired response. For the printers hence, a CMY(K)→CIELAB mapping represents a forward characterization transform while the CIELAB→CMY(K) map is an inverse transform. Herein the characterization color transform will be used to refer unambiguously to the forward transform; suitable inversion methods can be used to derive the corresponding inverse transform. The characterization transform is of immense value in many color management tasks such as derivation of ICC profiles for the printer, printer calibration, color control, etc.
The most popular technique to build a printer characterization transform involves printing and measuring a large set of color samples, i.e. CMY(K) patches, in conjunction with mathematical fitting and interpolation to derive CMY(K)→Lab mappings. The accuracy of the characterization transform clearly depends on the number (N) of patches printed and measured. Crucially, note that these patches correspond to contone CMY digital values, i.e. their binary representation is halftone dependent. Hence, deriving characterization transforms for a printer equipped with M halftone screens, requires N*M patches. Even for modest choices of N, M, e.g. N=1000, M=4, this number grows to be unmanageable. Note N cannot be made very small without compromising accuracy. As multiple media are thrown into the mix, the number scales further with the number of distinct media employed, i.e. N×M×P patches are needed where P distinct media types are used.
One aspect of patch measurement comprises generating multiple full-width patches of a single density across the whole printer page or photoreceptor belt i.e. the entire cross-process position of the printing machine. Such full-width patches are measured by a full-width array sensor during cycle up to obtain a printer model of the tone reproduction curve (TRC) at each inboard to outboard pixel column. The measurement can then show the amount of toner or print ink printed at each position on that page by the particular halftone screen then used. Any variations in the TRCs (i.e. variations from the true intended color) are compensated for in the image path via remapping of the halftone TRC. Such remapping is typically referred to as spatially varying TRC corrections (SVT) and these remappings are unique for each color and each halftone screen.
As noted above, as the number of selectable halftone screens increase for a particular device, the time it would take to print and measure patches for each halftone screen to build corresponding SVTs would quickly exceed any cycle up time targets. Since the necessary patches are also measured during run time, the time between SVT updates for any one screen depends on the number of screens that are being measured.
There is thus a need for a system for correlating uniformity compensations across different halftone screens that eliminates the need for separate uniformity measurements to be made on each halftone screen needing compensation for acceptable accuracy. Correlation would enable a reduced set of measurements to be made, thereby enhancing efficiency in the printer characterization process.